Automation 2.0 How AI Transforms Business Processes

By Jan Bosch, research center director, professor, consultant, and angel investor in startups.

The Rise of Automation 2.0

In the era of Automation 2.0, businesses are leveraging AI agents and LLMs to automate complex steps in processes that were previously impossible to handle without human intervention. Examples include:

• HR: Candidate selection and prioritization based on CVs and application letters.
• Finance: Transaction classification.
• Product Planning: AI-assisted decision-making.

While not all tasks are fully automated, AI can reduce human effort by an order of magnitude.

Key Challenges

1. Implicit Processes: Many companies lack explicit process models, relying on ambiguous, context-dependent steps that only humans can navigate. For AI integration, clear, detailed process models are essential.

2. Integration Complexity: AI systems must interface with CRM solutions, databases, and other tools, often lacking APIs or clear data semantics. This requires significant technical effort.

3. Validation and Trust: Defining what constitutes a "good" AI output is tricky, especially in regulated industries where human oversight is critical. Cultural resistance to AI-driven decisions remains a hurdle.

Enablers for Success

1. Tool Integration: Companies must invest in richer APIs and tools like OpenAI’s function calling, Microsoft Copilot, and Langchain to streamline AI-system interactions.

2. Retrieval-Augmented Generation (RAG): Providing domain-specific context to LLMs improves output quality. This requires robust data pipelines and clear data semantics.

3. Human-in-the-Loop: In high-stakes scenarios, human oversight builds trust. Two patterns emerge: